NVIDIA's leadership position in the field of artificial intelligence is undeniable. Recently, the company's quarterly financial report revealed that NVIDIA sold $31 billion worth of AI systems in the last quarter. Jensen Huang, co-founder and CEO of the company, emphasized the importance of AI applications across various industries during a conversation with Wall Street analysts. In this discussion, Huang mentioned Google's NotebookLM tool, stating that he uses this AI tool 'very frequently'.

The rapid development of deep learning relies on the scale of datasets, models, and computational power. In the fields of natural language processing and computer vision, researchers have already discovered a power-law relationship between model performance and data scale. However, in the field of robotics, especially in robot manipulation, such scalable patterns have yet to be established. A research team from Tsinghua University recently published a paper exploring data scaling laws in robot imitation learning and proposed an efficient data collection strategy that collected sufficient data in just one afternoon, enabling...

Shanghai Mita Network Technology Co., Ltd. recently announced that its AI-driven search platform—Mita AI Search—has been updated and now features a new topic functionality, allowing users to upload materials to the knowledge base and customize data sources for personal or professional fields. Users can create a personalized search environment tailored to their needs through Mita AI Search, enhancing search efficiency and relevance.

According to the latest report from the Tony Blair Institute, artificial intelligence (AI) is expected to replace between 1 million and 3 million jobs in the UK private sector. However, despite a certain degree of unemployment in the short term, in the long run, the increase in unemployment will likely be limited to several hundred thousand, as the emergence of new technologies will create new job opportunities. The institute predicts that in the coming decades, between 60,000 and 275,000 jobs will be replaced each year.

The Massachusetts Institute of Technology (MIT) showcased a brand new robot training model this week that abandons the previous focus on training methods centered around specific datasets, opting instead for an approach that utilizes vast amounts of information similar to that used in training large language models (LLMs). Researchers noted that imitation learning—where agents learn by mimicking individuals performing tasks—can fail when facing minor challenges. These challenges may include varying lighting conditions, different environmental setups, or new obstacles. In such cases, the robots lack sufficient data to adapt to these changes.

This week, MIT showcased a new model for training robots, aimed at addressing the challenges faced by imitation learning when introduced to small obstacles. Researchers pointed out that imitation learning can fail in conditions such as varying lighting, different environments, or new obstacles because robots simply do not have enough data to adapt. The team sought powerful data methods, like those used in models such as GPT-4, to tackle the problem. They introduced a new architecture called the Heterogeneous Pretrained Transformer (HPT), which aggregates information from different sensors and environments.

Is AI actually starting to ‘get smarter’?! The latest research from MIT indicates that the internal structure of large language models (LLMs) astonishingly resembles that of the human brain! This study utilized sparse autoencoder techniques to conduct an in-depth analysis of the activation space of LLMs, revealing three hierarchical structural features that are truly remarkable: first, at the microscopic level, researchers discovered the existence of structures akin to ‘crystals’. The surfaces of these ‘crystals’ are composed of parallelograms or trapezoids, similar to familiar lexical analogies such as ‘man:’

In August, Elon Musk's AI startup xAI promised to launch a generative AI model API named Grok, and this promise has finally come to fruition. However, the current API is relatively simple in functionality. Currently, xAI's API only offers one model, 'grok-beta', priced at $5 per million input tokens (approximately 750,000 words) or $15 per million output tokens. Tokens are essentially raw data.

Recently, OpenAI quietly released a statement on its official website, committing not to use its patents in an infringing manner. OpenAI stated that they would adhere to the principles of 'broad access' and 'collaboration', emphasizing that they would 'only use patents for defensive purposes'. However, this commitment has raised concerns among many experts. According to OpenAI's statement, their patents will be used defensively only when others do not threaten them, make claims, initiate lawsuits, or assist others in such activities. Their wording seems to suggest...

In the world of artificial intelligence, the reasoning capabilities of machine learning models, particularly large language models (LLMs), have been a focal point for scientists. Recently, Apple's AI research team published a paper titled 'Understanding the Limitations of Large Language Models in Mathematical Reasoning,' shedding light on these models' limitations when addressing logical problems. In the paper, researchers demonstrate this through a simple mathematical question. They first posed a question about Oliver picking kiwis: as follows: Oliver picked 44 on Friday.

Apple is set to launch its first AI feature - Apple Intelligence, for iPhone, iPad, and Mac devices. However, with the advancement of AI technology, privacy concerns have become increasingly important. So, how does Apple Intelligence protect user privacy? Apple's privacy protection strategy prioritizes on-device processing, followed by private cloud computing. This approach has two main advantages: first, on-device processing is faster.

On the stage of scientific research, MIT's team has just introduced a new 'Research Agent' - SciAgents, an AI system capable of conducting research autonomously. Its powerful capabilities are truly impressive. In research on bio-inspired materials, SciAgents revealed some previously considered unrelated interdisciplinary connections, successfully achieving a scale, precision, and exploratory capacity that surpass conventional human research. SciAgents is a 'super assistant' in the research community. This intelligent system can independently read literature, confirm